Abstract

The identification of substantively similar policy proposals in both proposed and adopted legislation is important to scholars of public policy diffusion and legislative politics. Conventional, manual, approaches are prohibitively costly in constructing datasets that accurately represent policymaking across policy domains, geographic units, and/or time. We propose the use of text-sequencing algorithms, applied to legislative text, to identify bills that introduce similar policy proposals. We present three ground truth tests, applied to a corpus of 500,000 bills from US-state legislatures. First, we show that bills introduced by ideologically similar sponsors are more likely to exhibit a high degree of text reuse. Second, we show that bills classified by the National Council of State Legislatures as covering the same policies exhibit a high degree of text reuse. Third, we show that rates of text reuse across state borders correlate strongly with the diffusion networks recently introduced by Desmarais, Harden and Boehmke (2015).